SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.

The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be "severe" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the "mild" category (severity probability <0.5) had an average Ct of 20.4 (P=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = -0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) (P=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.

Mass COVID-19 patient screening using UvsX and UvsY mediated DNA recombination and high throughput parallel sequencing.

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), also known as 2019 novel coronavirus (2019-nCoV), is a highly infectious RNA virus. A percentage of patients develop coronavirus disease 2019 (COVID-19) after infection, whose symptoms include fever, cough, shortness of breath and fatigue. Acute and life-threatening respiratory symptoms are experienced by 10-20% of symptomatic patients, particularly those with underlying medical conditions. One of the main challenges in the containment of COVID-19 is the identification and isolation of asymptomatic/pre-symptomatic individuals. A number of molecular assays are currently used to detect SARS-CoV-2. Many of them can accurately test hundreds or even thousands of patients every day. However, there are presently no testing platforms that enable more than 10,000 tests per day. Here, we describe the foundation for the REcombinase Mediated BaRcoding and AmplificatioN Diagnostic Tool (REMBRANDT), a high-throughput Next Generation Sequencing-based approach for the simultaneous screening of over 100,000 samples per day. The REMBRANDT protocol includes direct two-barcoded amplification of SARS-CoV-2 and control amplicons using an isothermal reaction, and the downstream library preparation for Illumina sequencing and bioinformatics analysis. This protocol represents a potentially powerful approach for community screening of COVID-19 that may be modified for application to any infectious or non-infectious genome.

Variants in SARS-CoV-2 associated with mild or severe outcome.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic is a global public health emergency causing a disparate burden of death and disability around the world. The viral genetic variants associated with outcome severity are still being discovered. METHODS: We downloaded 155 958 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from GISAID. Of these genomes, 3637 samples included useable metadata on patient outcomes. Using this subset, we evaluated whether SARS-CoV-2 viral genomic variants improved prediction of reported severity beyond age and region. First, we established whether including genomic variants as model features meaningfully increased the predictive power of our model. Next, we evaluated specific variants in order to determine the magnitude of association with severity and the frequency of these variants among SARS-CoV-2 genomes. RESULTS: Logistic regression models that included viral genomic variants outperformed other models (area under the curve = 0.91 as compared with 0.68 for age and gender alone; P < 0.001). We found 84 variants with odds ratios greater than 2 for outcome severity (17 and 67 for higher and lower severity, respectively). The median frequency of associated variants was 0.15% (interquartile range 0.09-0.45%). Altogether 85% of genomes had at least one variant associated with patient outcome. CONCLUSION: Numerous SARS-CoV-2 variants have 2-fold or greater association with odds of mild or severe outcome and collectively, these variants are common. In addition to comprehensive mitigation efforts, public health measures should be prioritized to control the more severe manifestations of COVID-19 and the transmission chains linked to these severe cases.Lay summary: This study explores which, if any, SARS-CoV-2 viral genomic variants are associated with mild or severe COVID-19 patient outcomes. Our results suggest that there are common genomic variants in SARS-CoV-2 that are more often associated with negative patient outcomes, which may impact downstream public health measures.